Proceeding of The Symposium on Data Science (SDS)
Symposium of Data Science (ISSN online: 2808-1846) aims to provide a forum for scholars and lecturers to discuss and exchange ideas about their research. In addition, providing opportunities for students to present student research projects in solving financial problems and related problems using a data science approach. In this activity, all participants are encouraged to be creative so as to generate ideas and innovations in the participants' research. The scope of this event is, but not limited to, in the fields of : application of data science in finance; actuarial science; Finance; and risk management. Â
Forecasting Weekly Stock Price of Microsoft Corporation (MSFT) Using ARIMA Box-Jenkins Method
The dynamic movement of stock prices in the financial market demands the ability to forecast future trends, particularly for investors and institutions aiming to make strategic decisions. This study focuses on forecasting the weekly stock price of Microsoft Corporation (MSFT) using the Autoregressive Integrated Moving Average (ARIMA) method, following the Box-Jenkins approach. The data used in this research consist of 54 weekly closing prices from April 29, 2024 to April 29, 2025. Several ARIMA models were tested, and the ARIMA(0.1.1) model was identified as the most suitable based on its accuracy metrics. This model achieved the lowest error values, including a Mean Absolute Percentage Error (MAPE) of 2.37%, indicating a high level of prediction accuracy. The results of this study provide useful insights for investors and financial analysts to better anticipate stock price fluctuations and enhance data-driven decision-making in portfolio management and investment strategies.
Forecasting the Weekly Stock Price of Apple Inc. (AAPL) using Autoregressive Integrated Moving Average Method
The stock price of Apple Inc. (AAPL) has been a subject of significant interest due to its volatility and market dynamics. In this study, we aimed to forecast the stock price of Apple Inc. from April 10, 2024-April 10, 2025, using the ARIMA(0,1,3) model. This model was selected due to its ability to capture key patterns in the time series data while minimizing forecasting errors. After differencing the data to ensure stationarity, the ARIMA model was fitted, incorporating two autoregressive (AR) terms and two moving average (MA) terms. Residual analysis, including the Ljung-Box and Shapiro-Wilk tests, confirmed that the residuals were uncorrelated and normally distributed, validating the model's appropriateness. The ARIMA(0,1,3) model produced reliable forecasts with minimized error metrics, offering a robust method for short-term stock price predictions. This study demonstrates the potential of ARIMA models in financial forecasting, particularly in predicting stock prices with a reasonable degree of accuracy.
Copyright (c) 2025 Sandrina Alya Harvinanda
Forecasting Weekly Stock Price of PT. Bank Central Asia Tbk (BBCA) Using ARIMA Box-Jenkins Method
The dynamic fluctuation of stock prices in financial markets highlights the importance of accurately forecasting future price movements, particularly for investors and financial institutions making strategic decisions, This study focuses on forecasting the weekly stock price of PT Bank Central Asia Tbk (BBCA) using the Autoregressive Integrated Moving Average (ARIMA) method based on the Box-Jenkins approach. The dataset consists of weekly closing stock prices from January 7, 2024, to December 29, 2024, totaling 51 observations obtained from Investing.com. Following data preprocessing, including stationarity testing and model selection. through ACF and PACF analysis, several ARIMA models were evaluated. The ARIMA(2,2,0) model was identified as the best-fitting model based on error metrics, achieving a Mean Absolute Percentage Error (MAPE) of 2.29%. This result demonstrates a high level of forecasting accuracy, providing valuable insights for investment planning and financial risk management. The findings confirm that ARIMA remains a reliable and practical method for short-term stock price forecasting in dynamic financial environments.
Copyright (c) 2025 Dheeya Raja Noorlesmana
Value-at-Risk Calculation for Banking Portfolio using Variance-Covariance and Markowitz Optimization
Risk measurement and investment decisions are closely related because investors try to strike a balance between possible returns and financial market uncertainties. The purpose of this study is to use Markowitz Portfolio Optimization and the Value at Risk (VaR) approach to evaluate and quantify the risk of an Indonesian banking stock portfolio that is listed on the LQ45 index. The study examines data from November 2024 to January 2025 to see how the portfolio's closing prices are affected by the portfolio structure determined through Markowitz Optimization. The Variance-Covariance method is then used to calculate the risk of the optimized portfolio. According to the VaR calculation using the Variance-Covariance method at two confidence levels, the maximum daily loss risk estimate is IDR 2,404,894.13 at the 95% confidence level and IDR 3,401,287.66 at the 99% confidence level. The backtesting results also show that the number of failures at both confidence levels is less than the chi-square critical value, indicating that the null hypothesis is not rejected. Based on these results, it is clear that the combination of Markowtiz Optimization and VaR calculation using Variance-Covariance works well and supports one another.
Copyright (c) 2025 Angelique Melinda Rohsinarni Sumbayak
Forecasting Weekly Stock of PT. Astra International Tbk (ASII) using ARIMA Model
Abstract— PT. Astra International Tbk (ASII) is one of the listed companies is (Indonesia Stock Exchange) IDX which focuses in multiple sectors. Stock is one of the important factors in the economy. This paper forecast the weekly stock of ASII from April 6, 2025 until April 27, 2025 by using the data from May 05, 2024 to March 23, 2025. By using Arima Box-Jenkins, the best ARIMA model for this data is the ARIMA (1,1,1) with the MSE value of 24463.64, RMSE of 156.4086, MAE value of 146.184 and MAPE of 3.0737%.
Forecasting Weekly Stock Price of Apple Inc by using ARIMA model
Stock price forecasting remains a critical field in financial research, offering practical benefits for portfolio optimization and risk management. This study focuses on forecasting the weekly closing prices of Apple Inc. (AAPL) from March to April 2025 using the Autoregressive Integrated Moving Average (ARIMA) model. The objective is twofold: to assess the forecasting accuracy and to validate the statistical adequacy of the model. Employing the Box-Jenkins methodology, the process includes stationarity testing via the Augmented Dickey-Fuller test, model identification through ACF and PACF plots, parameter estimation, and residual diagnostics using the Shapiro-Wilk and Ljung-Box tests. ARIMA(6,1,0) was selected based on the lowest AIC and BIC values and diagnostic compliance. The model achieved a low Mean Absolute Percentage Error (MAPE) of 2.6%, indicating strong predictive accuracy. Forecasts were accompanied by 95% confidence intervals, enhancing interpretability. The results confirm that the ARIMA model is suitable for short-term financial forecasting where model transparency and statistical validity are essential.
Copyright (c) 2025 Auriellia Theresia Kusmanto
Prediction of Weekly Stock Price of PT Indofood CBP Sukses Makmur Tbk (ICBP) with ARIMA Box-Jenkins Method
This research aims to forecast the weekly stock prices of PT Indofood CBP Sukses Makmur Tbk (ICBP) using the ARIMA model based on the Box-Jenkins methodology. Weekly closing price data from January 1 2024 to January 26 2025 is utilized as the basis for analysis. The data is first tested for stationarity, and appropriate differencing is applied to stabilize the mean. The ARIMA(2,1,0) model is identified as the best-fitting model. Model diagnostics, including the Ljung-Box test, confirm that the residuals exhibit white noise characteristics, indicating a good model fit. Forecasting performance is assessed using the Mean Absolute Percentage Error (MAPE), which resulted in a value of 1.58%, suggesting a high level of forecasting accuracy. The findings demonstrate that the ARIMA model is effective for short-term stock price prediction and can serve as a useful tool for investors and financial analysis.
Forecasting Weekly Stock Price of PT Ciputra Development Tbk. (CTRA.JK) Using ARIMA Box-Jenkins Method
The volatility of stock prices in the market demands the ability to predict future prices accurately which helps investors and companies to maximize the profits by risk management and investments allocation, PT Ciputra Development Tbk. is one of the leading property and real estate companies in Indonesia, offering modern concepts. This study focuses on forecasting the weekly stock price of PT Ciputra Development Tbk. using historical weekly closing stock price data from January 01, 2024 to December 31, 2024. The forecast utilized Autoregressive Integrated Moving Average (ARIMA) model. Based on the calculations, it is acquired that ARIMA (2, 2, (0) is the best and most efficient model to predict the future stock price, as it achieved the highest accuracy, indicated by the mean absolute percentage error (MAPE) value at 3.213681%.
Copyright (c) 2025 Ivana Felixia Suharsono
Forecasting Weekly Stock Price of Jakarta Stock Exchange Composite Index (IHSG) Using ARIMA Box-Jenkins Method
Stock price movements in the capital market reflect investor sentiment and economic trends, requiring accurate forecasting methods to support decision-making. One method that can be used for forecasting is the Autoregressive Integrated Moving Average (ARIMA). The application of the ARIMA method to the weekly stock price data of the Jakarta Stock Exchange Composite Index (IHSG) from November 2023 to April 2025 produces the equation Y, 0.874-1-0.5359+0.66193 + 0.041661-1+0.9721e2+ 0.1892-3+ et which is obtained from ARIMA(2,1,3) as the best model based on the lowest error values (MAE, MSE, RMSE, and MAPE). This model passed residual diagnostics and demonstrates strong potential in forecasting IHSG stock prices.
Copyright (c) 2025 Geovani F Simanjuntak
Forecasting Weekly Stock Price of Gold Futures (GCM5) Using ARIMA Box-Jenkins Method
The pattern of gold futures prices demands accurate forecasting to support rational decision-making on the part of investors and market experts. The study applies the ARIMA (Autoregressive Integrated Moving Average) model using the Box-Jenkins method for forecasting the weekly Gold Futures stock price (GCM5). The model was fit with 52 weekly closing prices from May 5, 2024, to April 27, 2025. For stationarity. Augmented Dickey-Fuller (ADF) test was conducted and then differencing. ACF, PACF, and "auto.arima(data)" were used to identify the model, and ARIMA(0,1,1) was found to be the best-fitting model. Diagnostic tests like Shapiro-Wilk and Ljung-Box tests confirmed the model fitness with residuals normally distributed and not having any serious autocorrelation. The forecast accuracy was checked using the Mean Absolute Percentage Error (MAPE), which yielded a value of 0.72%, indicating a very precise prediction. These results confirm that the ARIMA(0,1,1) model is a good representation of the trend and volatility of gold futures prices. This study provides real-world information to market participants and demonstrates the continued relevance of traditional time series models to financial forecasting.
Copyright (c) 2025 Naufal Pradipta Adriansyah
Forecasting Weekly Stock Price of PT Gudang Garam Tbk (GGRM) Using ARIMA Box-Jenkins Method
Stock price change prediction ability is paramount in directing investment decision-making in the dynamic financial environment. In this study, Box-Jenkins and the Autoregressive Integrated Moving Average (ARIMA) model are applied in predicting the monthly stock price of PT Gudang Garam Tbk (GGRM). Closing prices every month from January 1, 2024, to January 1, 2025, make up the dataset. It was discovered that the ARIMA (0.1.0) model was the most suitable based on the study; it achieved a satisfactory level of forecasting accuracy with a Mean Absolute Percentage Error (MAPE) of 3.7%. In empowering the analysts and investors to make sense of potential price trends, the research findings are in a position to empower them to make more informed investment decisions.
Copyright (c) 2025 Farabi Alqurthuby Fahlevi
Application of the Box-Jenkins ARIMA Method in Forecasting Weekly the Stock Price of PT Indofood Sukses Makmur Tbk
PT Indofood Sukses Makmur Tbk (INDF.JK) is the highest-ranked Indonesian food company with a diversified spread in some of the biggest sectors such as consumer branded food, flour mills, and agro-businesses. As one of the major forces behind the country's economy, the fluctuation of PT Indofood's share price is a serious. indicator to both investors and financial analysts. The Box-Jenkins ARIMA method is used in this study to forecast PT Indofood Sukses Makmur Tbk's stock price weekly from January 1, 2021, to September 24, 2023. Past stock price data were used to determine the best ARIMA model to use in forecasting future stock prices. The ARIMA (0.2.1) was identified as the most accurate model with the lowest MSE of 41366,51, RMSE of 203,3876, and MAPE of 2.6795%. This result is meaningful information to aid investors and companies in forecasting stock price change, facilitating more informed and strategic investment planning.
Copyright (c) 2025 Rosda Renata Kezia Simanjuntak
Forecasting Weekly Stock Price of PT. Bank Negara Indonesia Tbk (BBNI) Using ARIMA Box-Jenkins Method
Dynamic stock price movements in the economic world demand the ability to predict future trends, especially for investors and companies that make strategic decisions. This research focuses on forecasting the weekly stock price of PT Bank Negara Indonesia Tbk (BBNI) using the Autoregressive Integrated Moving Average (ARIMA) method based on the Box-Jenkins approach. The data analyzed consists of weekly closing prices from January 6, 2024 to November 2, 2024 as many as 44 observations. The results show that the ARIMA (2,1,0) model is the best fit for this dataset, with relatively highly accurate prediction results, as indicated by a Mean Absolute Percentage Error (MAPE) of 4.09%. These results offer valuable insights to help investors and companies anticipate stock price changes, supporting more informed and strategic investment planning.
Forecasting Weekly Stock Price of PT. Bank Negara Indonesia Tbk Using ARIMA Method
This study focuses on forecasting the stock price of PT. Bank Negara Indonesia Tbk (BBNI) using the Autoregressive Integrated Moving Average (ARIMA) method. The data used are weekly stock prices from March 3, 2024, to February 23, 2025, with a total of 51 observations. The model aims to forecast stock prices for the next four weeks, from March 2 to March 23, 2025. The Box-Jenkins method is used to identify and evaluate the best-fitting ARIMA model. After conducting stationarity tests, ACF and PACF plots, and performing residual analysis, the ARIMA (1,2,1) is chosen to be the best model. This model gives the lowest error, with a Mean Absolute Percentage Error (MAPE) of 8.58% indicating very accurate predictions. The results of this study show that the ARIMA model is reliable for short-term stock price forecasting which offers valuable insight to help investors. make better decisions by providing a simple but effective forecasting tool.
Copyright (c) 2025 Celia Christy Merlinda Tantono
Forecasting Daily Gold Price Using ARIMA Box-Jenkins Method
In this research, the ARIMA time series model is utilized to make predictions of gold prices from March 1, 2025, to May 12, 2025. The prediction of the movement of the price of gold from May 13 to May 17, 2025, is intended. A number of ARIMA settings were tested manually whereby the order of differencing was. designed to be two to deal with non-stationarity. Using AIC and BIC as criteria, the best models were the ARIMA (4,2,0) and ARIMA (4,2,1). These models produced short-term forecasts. The approach seeks transparency in model selection as well as interpretability in order to facilitate informed forecasting.
Copyright (c) 2025 Fernando William Alexander
The Application of Black Scholes Model for Option Pricing from Stock Price of Amazon Inc. (AMZN)
Stocks are easily available on numerous platforms, are frequently utilised in financial markets, and can be a viable choice for organisations seeking to raise capital. Furthermore, investors frequently choose stocks as investments since they have the potential to provide big returns. Black-Scholes Option Pricing Model In this study, we will use technical analysis to forecast the weekly stock price of Amazon Inc. (AMZN) for 3 months from February 5th, 2024 to May 6th, 2024. There were and are attempts to extend the classical Black-Scholes model by introducing the randomness into the coefficients r, μ, σ (interest rate, drift and volatility, respectfully) or to describe them by SDEs, and so on, and then to find explicit or close-form formulas for the hedging strategies and option prices
Copyright (c) 2024 Faudzan Kamil, Muara Lysta Sirait
The Application of ARIMA Box-Jenkins Method for Forecasting the Weekly Stock Price of PT. Telkom Indonesia Tbk
The stock is one of the most popular financial market instruments. Conversely, because stocks offer an enticing level of profit, many investors prefer them as an investment. Nonetheless, stocks are a well-liked investment choice among investors due to their potential for earning huge profits. In this work, we will forecast the weekly stock prices of PT. Telkom Indonesia Tbk (TLKM.JK) for 5 weeks from March 4, 2024 to April 1, 2024. The Autoregressive Integrated Moving Average (ARIMA) method is a valuable tool for investors to utilize when forecasting the stock and making purchase choices. We use the historical weekly stock price data for PT. Telkom Indonesia Tbk (TLKM.JK) from February 27, 2023 to February 26, 2024 was collected from Yahoo Finance website to create a forecast. In this study, there’s 4 different ARIMA models, the analysis show that the ARIMA (1,2,1) is the best model
Copyright (c) 2024 Angelique Melinda Rohsinarni Sumbayak
Analysis of Stock Options on Meta Platforms Inc Using the Black-Scholes Method
A stock option is a financial instrument that allows the holder to buy or sell a company's stock at a fixed price (strike price) over a set period of time. Stock options are classified into two types: call and put options. A call option grants the holder the right to purchase shares, whereas a put option grants the right to sell shares. However, the holder is under no obligation to exercise the right. Many methods have been developed by professionals to help investors or shareholders analyze the share price of a company. One method is the Black Scholes Model developed by Fischer Black and Myron Scholes in 1973. This model provides a formula for calculating the theoretical value of call and put options on stocks. This research provides valuable insights for investors, analysts, and other stockholders in making investment decisions. This study aims to identify and analyze the call price and put price of the META Platform Inc using the Black-Scholes Merton method based on stock data on February 12, 2024 to May 10, 2024 or in a 3-month data interval. Data obtained from Yahoo Finance website. The results show that at the underlying price of 476.20, the call option price is 6.78982241, while the put option price is 0.286881255. The higher call option price compared to the put option price indicates that the market is more optimistic towards Meta Platforms, Inc.'s share price rising from 476.20. This may also reflect market volatility and more bullish investor sentiment
Copyright (c) 2024 Laurensia Rajagukguk, Muara Lysta Sirait
Forecasting the Weekly Stock Prices of the Apple Inc. using the Autoregressive Integrated Moving Average
Stocks have been the rising topic and popular investing instrument for many and chosen as an option for individuals and/or companies interested in potential high returns for their capital. Various theories and analyses have been developed to help individuals to forecast stock price. In this research, we will implement the Autoregressive Integrated Moving Average (ARIMA) to forecast the closing stock price of Apple Inc. (AAPL) from January 2023 to December 2023 with weekly intervals. The data yielded two different ARIMA models and the analysis shows that the (0,1,1) model is the most optimal, with MAPE of 2.28 %.
Forecasting the Weekly Stock Price of PT United Tractors Tbk (UNTR) using ARIMA Box-Jenkins Method
PT United Tractors Tbk (UNTR) is a leading distributor of heavy equipment in Indonesia, established on October 13, 1972. Since September 19, 1989, United Tractors has become a public company by listing its shares on the Indonesia Stock Exchange. The company runs its business through 5 business units, including construction machinery, mining contractors, mining, construction industry, and energy. Various theories and analyses exist to forecast stock prices, aiding investors in making informed decisions when managing stock portfolios. In this investigation, ARIMA (p, d, q) technical analysis is employed to forecast the stock price of PT United Tractors Tbk for the ensuing 5 weeks, spanning from April 3, 2023, to May 1, 2023. Utilizing historical stock price data from April 04, 2022, to March 27, 2023, sourced from the Yahoo Finance website, researchers conduct the forecasting process. Following evaluations of 6 ARIMA models, the optimal model identified is ARIMA (1,2,1). This model is deemed superior due to its minimal with Mean Squared Error (MSE) value of 6024701.711, smallest Root Mean Squared Error (RMSE) value of 2454.526779, Mean Absolute Error (MAE) value of 1819.644, and smallest Mean Absolute Percentage Error (MAPE) value of 7%
Copyright (c) 2024 Ni Kadek Gita Maharani, Edwin Setiawan Nugraha
Forecasting the Weekly Stock Price of Grab Holdings Limited using ARIMA Box-Jenkins Method
In the domain of financial markets, stocks are widely used, offering a viable avenue for companies seeking capital acquisition. Moreover, due to their potential to generate substantial returns, investors usually choose stocks as their preferred investment vehicle. Experts have compiled a series of theories and empirical studies aimed at predicting stock prices, with the main objective of enabling investors to make prudent and informed decisions in buying and selling endeavors, thereby reducing the associated risks. In this study, we will use the technical analysis of ARIMA (p,d,q) Box-Jenkins method to predict the weekly share price of Grab Holdings Limited (GRAB). For the purpose of this study, Yahoo Finance provided historical data of Grab Holdings Limited (GRAB) weekly share price from March 27, 2023 to March 25, 2024. Of the 16 ARIMA models generated from this study, the model with the smallest error estimator value is the best model. The results show that ARIMA (2,1,1) is the most effective and suitable model with the MAPE value 4,08% means that the the accuracy is 95,92% with the actual data
Copyright (c) 2024 Chindia Ayu Hendraningtyas, Edwin Setiawan Nugraha
Application of the Black-Scholes Model on Analyzing the Option Pricing for Stock Price of JPMorgan Chase & Co.
Stocks are utilized extensively in the financial markets and provide a good option for businesses looking to raise funds. Additionally, investors usually choose stocks as their preferred investment vehicle due to their potential for large returns. When it comes to stock option pricing, the Black-Scholes model is an effective instrument. The pricing of complex financial instruments saw a significant advancement in the early 1970s when Myron Scholes, Robert Merton, and Fisher Black used current stock prices, expected dividends, the option's strike price, expected interest rates, time to expiration, and expected volatility to determine the theoretical value of an option contract. The objective of this research is to analyse the pricing of stock options for JPMorgan Chase & Co. using the Black-Scholes model. The Black-Scholes model is employed to achieve this, incorporating variables such as the underlying stock price, expected dividends, strike price, interest rates, time to expiration, and volatility to determine the theoretical values of call and put options. The underlying stock price of JPMorgan Chase & Co. is 198.77, the call option price is 2.774935402, and the put option price is 0.183125114, according to research findings. This suggests that the call option price is higher than the put option price at this strike price
Copyright (c) 2024 Vito Tri Andhika, Muara Lysta Sirait
Forecasting Cameco Corporation Monthly Stock Price Using ARIMA Model
This paper forecasts the monthly stock price of Cameco Corporation using data sourced from Yahoo Finance, spanning from May 2018 to April 2023. The Autoregressive Integrated Moving Average (ARIMA) method is employed to predict the company's stock price. Among various models, ARIMA (2, 1, 2) is selected due to its lowest Akaike Information Criterion (AIC). The model forecasts the stock price based on historical data, achieving an error rate of 7.79903%, equivalent to an accuracy of 92.20097%, which is considered very good. This forecast provides investors with valuable insights into the future stock price.
Copyright (c) 2024 Kenrick Saputra Tedja, Edwin Setiawan Nugraha
Forecasting Of JPMorgan Beta Builder ETF Stock Prices January 2020-February 2020 Using Arima Model
Stock market volatility makes investors worried about potential losses. Accurate stock predictions can guide investors in developing strategies to reduce the risk of loss. In this study, the ARIMA model will be utilized to forecast the stock price of JP Morgan Canada Building for 5 weeks, from January 13, 2020, to February 10, 2020. Historical stock price data for BBCA, from April 1, 2019, to January 6, 2020, was collected from the Yahoo Finance website. The results show that ARIMA (1,1,0) is the best model with a MAPE value of 1.121%.
Copyright (c) 2024 Pelangi Cinta Kirana, Edwin Setiawan Nugraha
The Application of ARIMA Box-Jenkins Method for Forecasting the Daily Stock Price per Share of Hasbro, Inc.
Stock is a type of securities that gives stockholders the ownership in a company. The stock price can be predicted and forecasted with several techniques and formula. This research is aim to help forecast stock price using ARIMA Box-Jenkins Method which can predict a stock price using time series analysis. Research is done to the Hasbro, Inc. monthly stock closing price data from 1 May 2017 – 1 December 2022 for forecasting the stock closing price of its company for the next 3 month. The finding of this work is Â ARIMA (1,2,0) is the best model as the most accurate among the other model with a MAPE of 1.97%. Â The forecast result is shown that Hasbro, Inc. stock price will fall in the next 3 months.
Copyright (c) 2024 Arifian Zulfikar Baihaqi, Dadang Amir Hamzah
The Application of the Arima Box-Jenkins Method on Forecasting the Monthly Closing Stock Price per Share of Jakarta Stock Exchange (JKSE)
Jakarta Stock Exchange (JKSE) or Indonesia Composite Index (ICI) measures the overall listed stock prices on the Indonesia Stock Exchange. This study aims to provide the closing stock price forecast of the monthly JKSE closing price per share from March 1st, 2022 to March 1st, 2023, using the ARIMA Box-Jenkins method, and compare the accuracy of forecasting 12 months of the dataset with forecasting 6 months of the dataset. The study contributes to the body of knowledge on stock market forecasting and provides investors with valuable information to make informed investment decisions. The study has found the best model to use is ARIMA(1,2,1) which is considered as the best model since it satisfies both Saphiro and L-jung box test, as well as having the lowest AIC among all of the models. This study also shows that its accuracy will be better by forecasting less ahead to the future. Whereby forecasting 12 months ahead resulted to 124817.291 MSE, 353.295 RMSE, 1428221815 MAE, and 4.265 MAPE. Meanwhile, forecasting 6 months ahead resulted to 34385.384 MSE, 185.433 RMSE, 1415348102 MAE, and 2.206 MAPE. The study contributes to the body of knowledge on stock market forecasting and provides investors with valuable information to make informed investment decisions.
Copyright (c) 2024 Priscilia Priscilia, Agus Sopian Eka Hidayat
Forecasting Exchange Rate for USD/CNY : A Box Jenkins Approach with Auto Regressive Integrated Moving Average (ARIMA) Modeling
Currency exchange rates have an important impact on the economy and finance fields. Fluctuation between domestic and foreign exchange rate commonly happens because of certain things. Recession, inflations, export and import activities, political stability, and government debt can be certain reasons fluctuations could happen. In this study, Auto Regressive Integrated Moving Average (ARIMA) model is applied to predict exchange rates. The time series data used is about the percentage exchange rate of the Chinese Yuan Renminbi to the US Dollar provided weekly from February 2022 - February 2023 with a total of 54 data. The result shows that the ARIMA model (2,2,1) was the most accurate model among other ARIMAs for examining our data. The percentage of (Mean Absolute Percentage Error) MAPE ARIMA (2,2,1) is 1.48%
Copyright (c) 2024 Whitney Karundeng, Srave Chrisdes Antoro
Forecasting the Number of Goods Transported in Java Using ARIMA Models
As the most populated island in Java, the island needs to have very efficient out-of-town land transportation. The train is really important transportation in Java. To avoid an unexpected surge in delivering the goods, proper forecasting is required. ARIMA (Autoregressive Integrated Moving Average) model is a method that can be used to predict the number of goods transported in the future. In this analysis, ARIMA (1,1,0) is the best model to use because it has the smallest MAPE among the other model which is 66.6%. The objective of this analysis is to predict the number of goods so the train company can anticipate surges in delivering the goods and may be useful in handling the number of goods in the future by making efficient policies.
Copyright (c) 2023 Chelisca Natasha, Fauziah Nur Fahirah Suding
Analysis The Influence of Climate Factors With COVID-19 Recovery Rates in DKI Jakarta
COVID-19 is a disease caused by the SARS-CoV-2 virus. The ideal air for the growth of this virus is at a temperature of around 8-10°C with a relative humidity of 60-90%. From March 2020 to December 2021, there were 7,709,036 cases of recovering positive COVID-19 patients. One of the factors thought to affect the rate of recovery from COVID-19 is climate factors, including the average temperature, average relative humidity of the air, rainfall, length of time. solar radiation, and average wind speed. The cure rate for COVID-19 in DKI Jakarta tends to increase from May 2020 to July 2020 and decrease from July 2020 to December 2020. This research was carried out with a spatial analysis using the Poisson regression, negative binomial and generalized Poisson regression methods. Based on the AIC value model, the most suitable model for this model is the Generalized Poisson Regression. From this study, it was concluded that the climatic factors that had a significant effect on the recovery rate of COVID-19 in DKI Jakarta were the average temperature, the average relative humidity of the air, the duration of sunlight, and the average wind speed.
Copyright (c) 2023 Dimas Teguh Ramadhan, Shofiyah Calista Ricadonna, Syalwa Rastia Kaldi
Forecasting Indonesian Coffee Exports using ARIMA
Coffee is one of Indonesia's leading export commodities in the agricultural sector. Good coffee quality makes Indonesia one of the largest coffee exporting countries in the world. The overall volume of Indonesian coffee exports in the world has decreased from year to year. Therefore, forecasting the volume of Indonesia's coffee exports within the next few years with the ARIMA Box-Jenkins method will greatly assist in a better export planning function for various parties. In this paper, we found that by using ARIMA (2,2,1) model, we can get the best prediction with a MAPE of 4124,64293%. With this predicted data, various parties can use this overview of the projected performance of coffee commodities in the short and medium term to maintain competitiveness amid the dynamics of the coffee commodity.
Copyright (c) 2023 Veldelen Yaphira, Agus Sofian Eka Hidayat